Computers & Industrial Engineering最新文献

{"title":"Optimizing integrated train rescheduling strategies for diverse disruption scenarios using reinforcement learning","authors":"Haodong Yin ,&nbsp;Lina Liu ,&nbsp;Ximing Chang ,&nbsp;Hao Fu ,&nbsp;Jianjun Wu","doi":"10.1016/j.cie.2025.111329","DOIUrl":"10.1016/j.cie.2025.111329","url":null,"abstract":"<div><div>Urban rail transit systems are crucial for efficient urban transportation, but unexpected disruptions pose significant challenges to maintaining optimal train schedules. Previous studies on train timetable rescheduling (TTR) have been limited by single disruption scenario and lack of comprehensive strategies, often relying on single or dual rescheduling tactics. This paper develops an innovative approach using reinforcement learning to optimize TTR under diverse disruption scenarios. Unlike previous studies, our approach incorporates a broader range of rescheduling strategies, including normal operations, train holding, short turning, reverse running, delayed departure from depots, and their combined strategies, enhancing the model’s flexibility and adaptability. To tackle the complexity of the proposed model, we further propose a novel reward mechanism with primary and secondary reward functions in reinforcement learning. The model’s effectiveness and adaptability are validated in various disruption scenarios using real-world cases on Beijing Metro Line 19. Experimental results demonstrate that, in a 30-minute disruption scenario, the integrated rescheduling strategy reduces total train delays by 46.6 % and computation time by 13.7 % compared to a single rescheduling strategy.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111329"},"PeriodicalIF":6.7,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing economic and operational feasibility of a designed and lab demonstrated robotic platform for omnichannel logistics","authors":"Joyjit Bhowmick ,&nbsp;Sebastian Köhler ,&nbsp;Gideon Arndt ,&nbsp;Georg Fischer ,&nbsp;Manmit Padhy ,&nbsp;Kai Furmans ,&nbsp;Jennifer Pazour","doi":"10.1016/j.cie.2025.111304","DOIUrl":"10.1016/j.cie.2025.111304","url":null,"abstract":"<div><div>As omnichannel services, such as buy online pickup in store and home delivery, grow in popularity, many brick-and-mortar retailers have adopted a store fulfillment strategy, where the same inventory on the store shelves is used for both online and in-store customers. These omnichannel offerings shift the in-store logistics once done by shoppers to retailers. Thus, the focus of this work is to explore whether new material handling equipment has the potential to be deployed in a retail store environment to support omnichannel services. To do so, we designed and built a new picker-to-stock robotic platform to automate piece-level pick, sort, and place tasks in retail environments. Lab demonstrations of the robotic platform confirm the feasibility to robotically pick items from retail shelves and was able to achieve picking performance of 20 s per unit picked once in front of the shelf location. Then an agent-based simulation model is created to mimic a store’s logistical operations that integrates data from the robotic platform’s lab demonstrations and data from online and in-store customer demand. An iterative process determines the minimum amount of manual and robotic resources needed to operate the store that satisfies a given service level for online order fulfillment and replenishment tasks. Then to assess the economic viability of deploying such a robotic platform with the lab demonstrated values and with improved performance, these resource levels are then combined with operational metrics obtained from the simulation and various cost aspects via an economic analysis model. Computational experiments show that deploying the robotic platform for picking and restocking goods in a store environment is operationally and economically viable for retail grocery stores providing omnichannel services using a store fulfillment strategy.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111304"},"PeriodicalIF":6.7,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal order quantities of a multi-period inventory of compatible products","authors":"Doraid Dalalah","doi":"10.1016/j.cie.2025.111338","DOIUrl":"10.1016/j.cie.2025.111338","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Effectively managing perishable inventory that consists of a heterogeneous assortment of mutually compatible products presents a complex and computationally challenging problem in operations research and supply chain fields. This particular problem arises when certain products can serve as substitutes for others, an operation commonly observed in maintenance parts, electronics, pharmaceuticals, batteries, and various other industries. Unlike single-period/single-product inventory models, where closed form solutions like the classical newsvendor are available in the literature, the compatibility between multiple products across multiple planning periods has remained largely unexplored in previous research.&lt;/div&gt;&lt;div&gt;Thus, the objective of our study is to find the optimal order quantities of different product types in an inventory system to minimize shortages and expiration, taking into account the challenging aspects of perishability and the existence of compatible substitutes. To rigorously address the complexities of this inventory system, a mixed-integer linear programming (MILP) framework is developed to encapsulate the intricate structural and operational characteristics of the problem. The proposed model is designed to accommodate both deterministic and stochastic scenarios, enabling the derivation of exact and approximate solutions through the integration of advanced optimization and simulation-based methodologies.&lt;/div&gt;&lt;div&gt;For large-scale instances, we recognize the computational challenges that arise when using standard solvers. Consequently, a metaheuristic algorithm is developed, which is specifically designed to reduce the computational time required to solve big instances. By tackling the interplay between perishability, compatibility, and multi periods, our study pushes the boundaries of existing research and presents innovative solution to real inventory where product substitution is allowed.&lt;/div&gt;&lt;div&gt;The findings showed that less shortages and expiration result in the case of compatible products. Compatibility also results in less order quantities. For a single inventory period, compatibility demonstrates higher effect on reducing the expected shortage and spoilage. For the case of full compatibility where any product can serve as a substitute for the others, closed form solution can be found for single period planning, while in multiple period planning, MILP optimization is required to address the problem. Finally, when compatibility is not present, it will lead to independent demand, thereby making the classical newsvendor problem applicable to this special case, but even in this case, optimization will be required for multi-period.&lt;/div&gt;&lt;div&gt;The proposed model exhibits broad applicability across a wide spectrum of industrial and medical applications, including but not limited to management of spare parts inventory, pharmaceutical supply chains, perishable food products such as dairy and ready-to-eat meals, the automo","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111338"},"PeriodicalIF":6.7,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing matching radius for ride-hailing systems with dual-replay-buffer deep reinforcement learning","authors":"Jie Gao ,&nbsp;Rong Cheng ,&nbsp;Yaoxin Wu ,&nbsp;Honghao Zhao ,&nbsp;Weiming Mai ,&nbsp;Oded Cats","doi":"10.1016/j.cie.2025.111296","DOIUrl":"10.1016/j.cie.2025.111296","url":null,"abstract":"<div><div>The matching radius, defined as the maximum pick-up distance within which waiting riders and idle drivers can be matched, is a critical variable in ride-hailing systems. Optimizing the matching radius can significantly enhance system performance, but determining its optimal value is challenging due to the dynamic nature of ride-hailing environments. The matching radius should adapt to spatial and temporal variations, as well as to real-time fluctuations in supply and demand. To address this challenge, this paper proposes a dual-reply-buffer deep reinforcement learning method for dynamic matching radius optimization. By modeling the matching radius optimization problem as a Markov decision process, the method trains a policy network to adaptively adjust the matching radius in response to changing conditions in the ride-hailing system, thereby improving efficiency and service quality. We validate our method using real-world ride-hailing data from Austin, Texas. Experimental results show that the proposed method outperforms baseline approaches, achieving higher matching rates, shorter average pick-up distances, and better driver utilization across different scenarios.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"208 ","pages":"Article 111296"},"PeriodicalIF":6.7,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of artificial intelligence in mitigating risk in multi-stage agricultural supply chain networks","authors":"Jiayi Zhu,&nbsp;Lei Yang,&nbsp;Lipan Feng","doi":"10.1016/j.cie.2025.111332","DOIUrl":"10.1016/j.cie.2025.111332","url":null,"abstract":"<div><div>More firms are adopting artificial intelligence (AI) to assist agricultural decision-making, but it is unclear whether and how AI can mitigate risk in agricultural supply chain. Driven by this question, we construct a multi-period and multi-layer agricultural supply chain network equilibrium model, where the theory of variational inequalities is utilized as the methodology. In this model, farmers make decisions at planting and harvest stages under yield uncertainty, and processors and retailers have to cope with potential disruptions through contingency measures. AI is introduced into supporting planting decision, and players’ interactions and risk cascade effects are incorporated. First, we find that AI enables farmers to shift from short-sightedness to a forward-looking perspective, which effectively stabilizes supply and price fluctuations, thereby mitigating risks. Second, AI makes the supply chain decisions for intertemporal products closer to those for non-intertemporal products, benefiting farmers, firms, and consumers. Third, AI can mitigate supply chain risks for non-intertemporal and low substitutable agricultural products more effectively than for other types of products. Finally, AI can also be a double-edged sword in specific scenarios, but the complementarity between AI and cold chain sharing enhances the efficiency of agricultural supply chains during unexpected disruptions. The findings provide valuable insights for the AI providers and managers to select the optimal agricultural products and develop incentive strategies.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111332"},"PeriodicalIF":6.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MFMDepth: MetaFormer-based monocular metric depth estimation for distance measurement in ports","authors":"Xinqiang Chen ,&nbsp;Fei Ma ,&nbsp;Yuzheng Wu ,&nbsp;Bing Han ,&nbsp;Lijuan Luo ,&nbsp;Salvatore Antonio Biancardo","doi":"10.1016/j.cie.2025.111325","DOIUrl":"10.1016/j.cie.2025.111325","url":null,"abstract":"<div><div>Automated container guided vehicle (AGV) navigation systems that rely on magnetic pin infrastructure are currently facing serious sustainability challenges. The monocular metric depth estimation method is emerging as a promising alternative by aligning the predicted depth with the actual scale, however, its deployment in dynamic port environments (e.g., line of sight occlusion, texture loss) is still limited by precision and reliability tradeoffs. The proposed framework is implemented in three steps. Firstly, the framework initially introduces an innovative MetaFormer architecture, which incorporates a global squeeze block (GSB). This GSB employs a Squeeze Former (SF) to facilitate comprehensive modeling of inter-token relationships across the global image context. Secondly, the bins module, which combines wavelet transform convolution (WBM), is utilised to estimate the metric depth, and the backbone network is employed to estimate the relative depth. Finally, the framework fuses the two depths to achieve a refined metric depth estimation. Extensive evaluation shows that our method achieves approximately 26.3% RMSE performance improvement compared to MiDas, approximately 21.3% performance improvement on AbeRel metrics compared to SOTA model ZoeDepth. Compared to traditional baseline DS-SIDE, our approach achieves approximately two times improvement in depth prediction accuracy across all metrics, while maintaining competitive inference performance.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111325"},"PeriodicalIF":6.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resilient coordination of test sampling and requirement supply in the lab test supply chain: soft worst-case distributionally robust optimization","authors":"Hani Gilani ,&nbsp;Hadi Sahebi ,&nbsp;Tom Van Woensel","doi":"10.1016/j.cie.2025.111316","DOIUrl":"10.1016/j.cie.2025.111316","url":null,"abstract":"<div><div>Experiences from the global pandemic highlight the need to restructure blood sampling for CRP tests to adapt to varying levels of epidemic severity and operational risks. Additionally, since the tests rely on specific requirements, it’s crucial to establish a coordinated framework for both networks to ensure effective operations. This study develops a model comprising three subproblems to address the outlined problem. The first subproblem involves a blood sample collection model, focusing on regional coverage and route optimization under operational risks. The second subproblem assesses suppliers for the SC network of lab-test requirements, funneling top candidates to the third subproblem. The supply chain for CRP test requirements is established in the third subproblem, incorporating optimal test and sample numbers from Subproblem 1 and top suppliers from Subproblem 2. The proposed model is designed with a resilient structure to address pandemic challenges by incorporating redundancies in the supply process, minimizing unmet demand, and planning for capacity expansion. Infection rate uncertainty significantly impacts the CRP sampling model. To address this, the study introduces a distributionally robust optimization model with a soft worst-case approach, minimizing worst-case violations while reducing conservatism. In the second subproblem, suppliers are evaluated using a novel hybrid MCDM model. This model considers essential factors such as information capability, service quality, access speed, supplier capacity, supplier robustness, and economic advantage. To demonstrate the applicability of the proposed study, the model is applied to a case study in Tehran. The findings reveal the optimal locations, capacities, and types of facilities, their optimal paths within both networks, and their optimal interrelations with suppliers to meet demand requirements effectively. In addressing infection uncertainty, the proposed robust model demonstrated a clear reduction in conservatism regarding expectation violations. This allows for better control of risk aversion while maintaining a flexible, soft approach to modeling.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111316"},"PeriodicalIF":6.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Priority-oriented diet optimization problem for skin health under epistemic uncertainty","authors":"Mojtaba Nouri,&nbsp;Mir Saman Pishvaee","doi":"10.1016/j.cie.2025.111224","DOIUrl":"10.1016/j.cie.2025.111224","url":null,"abstract":"<div><div>Skin disorders significantly impact quality of life, affecting physical and psychological well-being. The Global Burden of Disease Study 2017 ranks skin diseases as the fourth leading cause of nonfatal disease burden worldwide. In the U.S., about 84 million people suffer from skin conditions annually, leading to substantial psychological distress and economic burden. Diet has a profound impact on skin health, with specific vitamins, minerals, and fatty acids playing crucial roles in maintaining skin integrity and preventing aging. The Mediterranean diet, in particular, shows promise for skin rejuvenation. Dietary supplements address nutritional deficiencies impacting skin health. Mathematical modeling, rooted in George Stigler’s 1945 work, offers a structured approach to diet planning, ensuring nutritional adequacy and cost-effectiveness. This research aims to optimize diet plans for skin health using supplements and mathematical modeling. A novel method called “Rolling priority-oriented planning” is introduced to prioritize food over supplements while addressing dietary deficiencies. Results indicate that dietary plans incorporating both food and supplements fail to meet all nutritional needs, highlighting the need for targeted supplement production. The proposed method increases the desirability of the adult female diet by 23.47 % and the adult male diet by 25.33 %, meeting nutrient needs from food sources. Nutrients are used in four priority categories to guide the production of dietary supplements, including first to third-level priorities and those with no priority assigned. This study identifies seven critical nutrients as top candidates for use as ingredients in new dietary supplements: dietary fiber, calcium, magnesium, iron, and vitamins A, D, E, and B3.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111224"},"PeriodicalIF":6.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A location-inventory model for perishable product supply chain network considering dual-channel retailing and quality degradation under front warehouse mode","authors":"Shuyuan Guo ,&nbsp;Hongtao Hu ,&nbsp;Lu Zhen ,&nbsp;Xinyue Guo","doi":"10.1016/j.cie.2025.111328","DOIUrl":"10.1016/j.cie.2025.111328","url":null,"abstract":"<div><div>The escalating demand for perishable product drives fresh e-commerce evolution. As the fresh e-commerce market matures, the emergence of front warehouses has revolutionized traditional warehousing and retail models. Considering the dual-channel retailing and the perishability of products, therefore, this paper investigates a location-inventory problem of perishable product supply chain network under front warehouse mode. First, the different demands of online and offline channels and the changes in freshness of the perishable products are analyzed. Second, with the goal of maximizing profits, a mixed-integer linear programming model is established, taking into account the service range division of different channel demands and the impact of product age on customer demand. Third, the enhanced hybrid heuristic algorithm, which integrates particle swarm optimization with adaptive large neighborhood search, is employed to augment the efficiency in addressing large-scale issues. Subsequently, to validate the feasibility and effectiveness of the proposed model and algorithm, a case study of Hema Fresh stores in China is conducted. Finally, the experimental results and sensitivity analysis show that the proposed model can provide suggestions for effectively reducing logistics costs and improving the service level of perishable product supply chain network.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111328"},"PeriodicalIF":6.7,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-server appointment scheduling under time-dependent no-shows and cancellations","authors":"Roksana Pourmohsen,&nbsp;Jing Shi","doi":"10.1016/j.cie.2025.111281","DOIUrl":"10.1016/j.cie.2025.111281","url":null,"abstract":"<div><div>Managing patient scheduling efficiently is essential for balancing healthcare resources, minimizing wait time, and reducing operational costs. This study develops a stochastic multi-server scheduling model designed to minimize costs while accounting for key uncertainties, including time-dependent no-shows, appointment cancellations, and variability in patient arrivals and service times. The model integrates an overbooking strategy and a structured approach to walk-in patient admissions, where the number of walk-ins is determined in advance based on expected cancellations. This structured planning enhances predictability and provides a more systematic scheduling process, with a manageable cost impact. The model is solved using the Sample Average Approximation (SAA) method. The results reveal that a 7% increase in cancellation rates can lead to an average system cost increase of 10%, accompanied by a 10% decrease in the number of bookings. Back-loaded arrival patterns are particularly costly, contributing to a 30% increase in costs compared to front-loaded arrival patterns. Additionally, when back-loaded arrival patterns coincide with decreasing no-show rates, where the highest no-show rates belong to the first appointment slot, the system experiences its highest costs. These findings highlight the substantial effect of patient flow dynamics on the efficiency and cost management of healthcare systems.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"207 ","pages":"Article 111281"},"PeriodicalIF":6.7,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}